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Record W2181280322 · doi:10.1002/jctb.4860

Applications of biofiltration in drinking water treatment – a review

2015· review· en· W2181280322 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Chemical Technology & Biotechnology · 2015
Typereview
Languageen
FieldEnvironmental Science
TopicWater Treatment and Disinfection
Canadian institutionsUniversity of GuelphCarleton University
Fundersnot available
KeywordsBiofilterBackwashingEnvironmental scienceWater treatmentWaste managementFiltration (mathematics)Environmental engineeringPulp and paper industryEngineering

Abstract

fetched live from OpenAlex

Abstract Biofiltration is a process in which an otherwise conventional granular filter is designed to remove not only fine particulates but also dissolved organic compounds through microbial degradation. Biofiltration can reduce the need for chemicals in drinking water treatment and thus improved applications of biofiltration in drinking water treatment can be viewed as green or sustainable engineering technology. Recent trends in biofiltration technology for drinking water treatment have or have attempted to extend the performance of biofilters through gaining a better understanding of operational constraints. This review articles summarizes important operational parameters influencing biofiltration performance such as hydraulic loading, empty bed contact time ( EBCT ), temperature, media type, and backwashing conditions. In addition, recent advancements in biofiltration operations including, ozonation, ammonia removal and the influence of nutrient (nitrogen, phosphorous) supplementation to facilitate carbon removal are explored. � 2015 Society of Chemical Industry

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.994
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.301
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it